FEEDBACK LOOP and PROMPT TUNING

[mayank-rakesh-mck]

A structured plan draft for implementing a feedback collection loop, prompt tuning with DSPY, and alignment learning for WrenAI, divided into phases:

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Phase 1: Setup and Planning

Objective: Establish a baseline infrastructure for feedback collection and initial tuning capabilities.

1. Define Goals & KPIs

   • Identify target metrics (e.g., SQL query success rate, accuracy of responses, user satisfaction, mouse clicks and copy interactions).
   • Define measurable KPIs for each stage of feedback and learning.

2. Initial Model and Prompt Design

   • Start with baseline prompt templates for Text-to-SQL generation.
   • Integrate the DSPY framework for scalable prompt tuning (use it for efficient testing, training, and feedback incorporation into prompt structures).

3. Feedback System Infrastructure

   • Todo:
     • Set up a user feedback interface for collecting qualitative and quantitative data from WrenAI users.
     • Implement a feedback rating system after each query (e.g., "Was this SQL result helpful?").
     • Store feedback in a centralized database for analysis and model improvement tracking.

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Phase 2: Feedback Loop Design and Integration

Objective: Build the feedback loop and begin the first cycle of tuning.

1. Data Collection from User Interactions

   • Todo:
     • Capture feedback from user queries and store metadata such as query intent, SQL output, and user corrections.
     • Integrate metadata from interactions into a feedback pipeline to continuously refine the model.
     • Develop an interface for user corrections where users can manually adjust SQL queries that were wrong.

2. Model Refinement (Prompt Tuning)

   • Utilize DSPY to modify and improve prompts based on user feedback.
   • Todo:
     • Develop a feedback evaluation system that flags prompt failures or inaccuracies.
     • Automatically retrain or fine-tune prompts with DSPY using new data from user feedback.
     • A example/shot selection pipeline for ICL.
     • Prioritize frequent queries for prompt optimization.

3. Build the Self-Learning Feedback Loop

   • Todo:
     • Implement a mechanism where feedback (e.g., corrected SQL queries or failure points) is fed back into a ICL pipeline.
     • Integrate an automated prompt adjustment system to improve query accuracy over time.

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Phase 3: Alignment Learning (for open source models)

Objective: Enhance model understanding through alignment and self-improving mechanisms.

1. Schema and Business Alignment

   • Introduce semantic modeling and metadata enrichment to help the model align SQL outputs with the business's unique terminology.
   • Todo:
     • Design an alignment learning system to map feedback and user corrections to business terms and data schema.

2. Train Model on Feedback-Driven Data

   • Todo:
     • Utilize reinforcement learning from user feedback to adjust alignment.
     • Periodically retrain models based on accumulated feedback data to better understand context and relationships within the data schema.

3. Error Recovery and Retraining

   • Todo:
     • Build a robust error recovery process: when a query fails, identify patterns in the feedback, and incorporate those into prompt tuning and training cycles.
     • Implement learning from both positive and negative feedback (e.g., successful SQL outputs and user-corrected failures).

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Phase 4: Advanced Feedback Loop Optimization

Objective: Achieve a continuous, self-improving feedback system.

1. Automate Feedback Analysis

   • Todo:
     • Implement automated analysis of feedback logs to detect trends and problem areas.
     • Prioritize improvements based on feedback with the highest occurrence or impact.

2. Continuous Prompt Tuning with DSPY

   • Regularly update the model using DSPY prompt tuning mechanisms, ensuring it adapts to evolving user needs and growing data complexity.
   • Todo:
     • Set up DSPY models to experiment with new prompts in real-time, measure their success, and adjust automatically.

3. Model Improvement Release Cycle

   • Todo:
     • Develop a cycle for releasing improved models based on feedback collection and tuning.
     • Ensure the model undergoes testing phases before deployment to prevent errors.

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Phase 5: Monitoring, Scaling, and Long-Term Maintenance

Objective: Scale the feedback loop and alignment learning for continuous improvements.

1. Monitoring and Reporting

   • Todo:
     • Set up dashboards that track performance improvements and errors over time.
     • Regularly report KPIs, query success rates, and other critical feedback metrics.

2. Scalable Alignment Learning

   • Todo:
     • As WrenAI scales, implement parallel processing for feedback, allowing faster tuning and error detection.

3. Long-Term Maintenance

   • Todo:
     • Periodically audit the model to ensure prompt tuning and alignment learning remain accurate.
     • Refresh feedback pipelines to adapt to changing data structures, user needs, or business goals.

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